I think it makes perfect sense for R to drop it since 'NA' represents
uninformative information. I do not know if there is a elegant solution
but I would suggest that you make these 'NA' into an informative value.
Here is one possibility:
df - data.frame( AA=1:10, BB=rep(1:5,2), CC=rep(1:2,5), DD=rnorm(10) )
df[ 9:10, CC ] - NA
df[is.na(df)] - lala ## change NA's into informative category ##
aggregate( df$DD, by=list( df$CC ), mean )
Group.1 x
1 1 1.1533763
2 2 0.6427338
3lala -0.2745249
aggregate( df$DD, by=list( df$BB, df$CC ), mean )
Group.1 Group.2 x
11 1 0.47264081
22 1 0.63795211
33 1 1.66756015
45 1 1.83535232
51 2 0.89914287
62 2 1.11102134
73 2 0.22268699
84 2 0.33808394
94lala -0.60154608
10 5lala 0.05249622
Regards, Adai
On Fri, 2006-02-24 at 10:16 -0500, Vivek Satsangi wrote:
Folks,
Sorry if this question has been answered before or is obvious (or
worse, statistically bad). I don't understand what was said in one
of the search results that seems somewhat related.
I use aggregate to get a quick summary of the data. Part of what I am
looking for in the summary is, how much influence might the NA's have
had, if they were included, and is excluding them from the means
causing some sort of bias. So I want the summary stat for the NA's
also.
Here is a simple example session (edited to remove the typos I made,
comments added later):
tmp_a - 1:10
tmp_b - rep(1:5,2)
tmp_c - rep(1:2,5)
tmp_d - c(1,1,1,2,2,2,3,3,3,4)
tmp_df - data.frame(tmp_a,tmp_b,tmp_c,tmp_d);
tmp_df$tmp_c[9:10] - NA ;
tmp_df
tmp_a tmp_b tmp_c tmp_d
1 1 1 1 1
2 2 2 2 1
3 3 3 1 1
4 4 4 2 2
5 5 5 1 2
6 6 1 2 2
7 7 2 1 3
8 8 3 2 3
9 9 4NA 3
1010 5NA 4
aggregate(tmp_df$tmp_d,by=list(tmp_df$tmp_b,tmp_df$tmp_c),mean);
Group.1 Group.2 x
1 1 1 1
2 2 1 3
3 3 1 1
4 5 1 2
5 1 2 2
6 2 2 1
7 3 2 3
8 4 2 2
# Only one row for each (tmp_b, tmp_c) combination, NA's getting dropped.
aggregate(tmp_df$tmp_d,by=list(tmp_df$tmp_c),mean);
Group.1x
1 1 1.75
2 2 2.00
What I want in this last aggregate is, a mean for the values in tmp_d
that correspond to the tmp_c values of NA. Similarly, perhaps there is
a way to make the second last call to aggregate return the values of
tmp_d for the NA values of tmp_c also.
How can I achieve this?
--
-- Vivek Satsangi
Student, Rochester, NY USA
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